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Low-overhead Communications in IoT Networks: Structured Signal Processing Approaches

169,38 
169,38 
2025-07-31 169.3800 InStock
Nemokamas pristatymas į paštomatus per 18-22 darbo dienų užsakymams nuo 19,00 

Knygos aprašymas

The recent developments in wireless communications, networking, and embedded systems have driven various innovative Internet of Things (IoT) applications, e.g., smart cities, mobile healthcare, autonomous driving and drones. A common feature of these applications is the stringent requirements for low-latency communications. Considering the typical small payload size of IoT applications, it is of critical importance to reduce the size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead communications also help to improve the energy efficiency of IoT devices. Recently, structured signal processing techniques have been introduced and developed to reduce the overheads for key design problems in IoT networks, such as channel estimation, device identification, and message decoding. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This book provides an overview of four general structured signal processing models: a sparse linear model, a blind demixing model, a sparse blind demixing model, and a shuffled linear model, and discusses their applications in enabling low-overhead communications in IoT networks. Further, it presents practical algorithms based on both convex and nonconvex optimization approaches, as well as theoretical analyses that use various mathematical tools.

Informacija

Autorius: Yuanming Shi, Jun Zhang, Jialin Dong,
Leidėjas: Springer Nature Singapore
Išleidimo metai: 2020
Knygos puslapių skaičius: 168
ISBN-10: 9811538697
ISBN-13: 9789811538698
Formatas: Knyga kietu viršeliu
Kalba: Anglų
Žanras: Machine learning

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